Scientist II, Computational Biology, Pharma R&D

Location: Boston, United States

Salary: $90,000–$135,000 in Chicago or $100,000–$150,000 in New York City; ranges may vary for other locations.

Company

hirify.global develops a proprietary precision-medicine platform that connects real-world evidence with AI to support clinical care, pharmaceutical research, and drug development.

What you will do

• Partner with pharmaceutical collaborators on computational research involving target discovery, biomarker development, and clinical development.
• Analyze genomic, transcriptomic, imaging, and clinical datasets to study trial design, patient selection, treatment response, resistance mechanisms, and disease biology.
• Use LLMs, foundation models, and agentic workflows to accelerate coding, research, documentation, review, and insight generation.
• Develop and implement methods for real-world, clinical, and omics data, including survival analysis, causal inference, and multimodal integration.
• Build reusable code, internal packages, and scalable solutions with Research, Clinical, Data Science, and Engineering colleagues.
• Communicate findings through reports, partner deliverables, presentations, manuscripts, and conference materials.

Requirements

• PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or a related field, or a master’s degree with 3+ years of relevant experience.
• Proficiency in R and/or Python and experience with computational biology and scientific computing libraries.
• Experience using machine learning, LLM-based coding assistants, and agentic frameworks for biological or clinical research.
• Experience with version control, modular code, documentation, SQL, and large relational databases.
• Strong knowledge of statistics, study design, real-world clinical data, and cancer biology, immunology, or human disease mechanisms.
• Experience analyzing large-scale biological datasets such as NGS, RNA-seq, genomics, or transcriptomics data, with strong written and verbal communication skills.

Nice to have

• Experience configuring or adapting LLMs for scientific work.
• Expertise in real-world evidence, survival analysis, causal inference, network or systems biology, or multimodal integration.
• Peer-reviewed publications or conference presentations.
• Understanding of the drug development lifecycle from target discovery to clinical development.

Culture & Benefits

• Work at the intersection of biological data science, AI, and translational research.
• Client-facing collaboration with major pharmaceutical partners.
• Benefits may include incentive compensation, restricted stock units, medical coverage, and other benefits.
• Equal opportunity employment is provided without discrimination based on protected characteristics.